Entropy-based link prediction in weighted networks**Project supported by the National Natural Science Foundation of China (Grant Nos. 61201173 and 61304154), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20133219120032), the Postdoctoral Science Foundation of China (Grant No. 2013M541673), and China Postdoctoral Science Special Foundation (Grant No. 2015T80556).

Abstract

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In a previous work [Xu et al. Physica A, 456 294 (2016)], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely weighted path entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three other typical weighted indices.